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By Laura Hamilton

Machine Learning (CS 229) is the most popular course at Stanford. Why? Because, increasingly, machine learning is eating the world. Machine learning is a powerful artificial intelligence tool that enables us to crunch petabytes of data and make sense of a complicated world. And it's transforming a wide variety of industries. It's solving previously unsolved problems.

Cutting-edge startups (as well as established tech companies and Universities) are increasingly finding new, novel, and exciting ways to apply powerful machine learning tools such as neural networks to existing problems in many different industries.

Below is a list of 10 of the most interesting applications.

#1: Automating Employee Access Control

Amazon, one of the pioneers of machine-learning based recommendation engines and price discrimination algorithms, launched a machine learning contest on Kaggle to determine whether it was possible to automate employee access granting and revocation. Amazon has a considerable dataset of employee roles and employee access levels. They're trying to develop a computer algorithm that will predict which employees should be granted access to what resources. According to Amazon, "These auto-access models seek to minimize the human involvement required to grant or revoke employee access."

#4: Identifying Heart Failure

researchers have found a way to extract heart failure diagnosis criteria from free-text physician notes. They developed a machine learning algorithm that combs through physicians free-form text notes (in the electronic health records) and synthesize the text using a technique called "Natural Language Processing" (NLP). Similar to the way a cardiologist can read through another physician's notes and figure out whether a patient has heart failure, computers can now do the same.

In the '90s and 2000s, software and the internet transformed the way that companies do business. Cutting-edge, tech-savvy companies such as Amazon and grew rapidly. Old, stodgy companies like Blockbuster and Borders failed to keep up.

In the 2010s and 2020s, powerful analytics and machine learning are transforming industries again, just as software transformed the world over the past 30 years.